Residual Echo Suppression Using Deep Learning

Adaptive Echo Cancellation (AEC) is a set of techniques meant to decrease the unwanted echo in an audio signal recorded by a communication device. Typically, noise and nonlinear distortions remain after AEC. Therefore, Residual Echo Suppressor (RES) is applied to improve upon AEC.
The goal of this project is to harness a deep learning approach to improve the RES.
Classical approaches that implement RES are far from being ideal. In this project, we ask the question: Does the deep learning approach perform better than the classical approaches?
To answer this question, we set up a data acquisition simple system, comprised of a microphone and a speaker. We collected a new dataset and trained several neural network (NN) architectures on it.
Our results show the feasibility of such a deep-learning RES approach. In some aspects, our RES outperforms other techniques.
We believe that better modeling of the actual communication scenario and a room acoustic response will help to acquire more precise dataset for training NN and, therefore, will improve results substantially.